Early Prediction and Evaluation of Risk of Autism Spectrum Disorders

Ashmawi, Nada S; Hammoda, Manal A

doi:10.7759/cureus.23465

Cited by 10 publications

(5 citation statements)

References 47 publications

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“…Social interaction disturbance is considered the most distinctive sign for Autism Spectrum Disorder over other developmental disorders. 23 The combination of impairments in social communication and interaction, with impairment in eye contact and protodeclarative and protoimperative pointing as well as weak joint attention and shared enjoyment, together with stereotyping/perseverative behaviours and sensory processing challenges, point strikingly towards this diagnosis.…”

Section: • the Modified Checklist For Autism In Toddlersmentioning

confidence: 99%

Screening for Developmental Behavioural Disorders

2023

SFP

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Developmental and Behavioural Paediatrics encompasses all neurodevelopmental and behavioural issues that children and adolescents may encounter across time. The developmental journey of each child starts at birth and screening should be done routinely during all well-child visits at the community medical practitioner level. It involves screening through routine developmental milestones and looking out for deviations as well as identifying red flags in any domain of development, including motor, speech and language, and social development. We screen in order to allow prompt identification and early intervention. The American Academy of Paediatrics recommends developmental screening to be done at 9, 18, and 30 months. Screening tools include the Denver Developmental Screening Test, Singapore as the baseline screening tool and other specific tools such as the Goodenough Draw a Man, the Ages & Stages Questionnaire, the Vanderbilt Rating Scales, and the Modified Checklist for Autism in Toddlers, Revised with Follow-up. Differentiation between Autism Spectrum Disorder and Attention Deficit Hyperactivity Disorder can be challenging at a young age. Differences need to be teased out through a carefully taken history of the early years. The Diagnostic and Statistical Manual -V now allows dual diagnoses, most commonly established in tandem around the age of six. Screening at around the age of five will thus be useful to look for ADHD as an occurring condition amongst those diagnosed with ASD. School readiness should also be looked at around this time.Better awareness and identification will then lead to prompt diagnosis and intervention in order to optimise eventual outcomes.

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Section: • the Modified Checklist For Autism In Toddlersmentioning

confidence: 99%

Screening for Developmental Behavioural Disorders

2023

SFP

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“…У результаті отримано прогностичну модель із розрахованою середньою похибкою апроксимації 13,2% і коефіцієнтом детермінації (R 2 ) -0,74, яка має такий вигляд: Y=32,30 + (-0,08)×ВП + (0,04)×SWI + + (-0,24)×ЧА + (-0,1)×АА + (1,97)×ЧЕ + + (0,02)×АЕ + (11,15)×ФАБ + (-4,60)×СКДБ + + (-16,26)×ФАВ + (-11,9)×ФАКМТ.…”

Section: результати дослідження та їх обговоренняunclassified

“…Зокрема, у дослідженні за участю 246 дітей з РАС показано, що у 80% дітей напади починалися на першому році життя. Епілептичний процес в мозку призводить до локальної гіпоксії, змін в експресії генів і вивільненні нейромедіаторів (зокрема, гамма-аміномасляної кислоти та глутамату), що призводить до порушень функціонування нейронних мереж, які забезпечують вищі психічні функції дитини [14][15][16][17].…”

Section: результати дослідження та їх обговоренняunclassified

Prediction of the risk of development of autistic spectrum disorders in children with epileptic encephalopathies

Miroshnykov

2023

MPU

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Predicting the risk of developing autism spectrum disorders (ASD) in children with epileptic encephalopathies (EE) is an important task, which makes it possible to identify the most significant risk factors and develop ways of their modification. Purpose - to develop a model for predicting the risk of developing ASD in young children with EE. Materials and methods. 75 children aged 0-3 years with the onset of epileptic seizures in the first year of life, clinical manifestations of EE were examined. The most informative clinical, neurophysiological, and neuroimaging indicators for predicting the risk of clinical manifestations of ASD were determined: age of onset of epileptic seizures, index of spike-wave activity during NREM sleep, frequency and amplitude of alpha rhythm, frequency and amplitude of epileptiform activity, fractional anisotropy and mean diffusion coefficient in the Broca’s center, fractional anisotropy in the Wernicke center and knee of the corpus callosum. The multiple linear regression method was used to predict the risk of developing ASD symptoms. Results. A model for predicting the risk of developing clinical manifestations of ASD in children with EE has been developed. It was established that the risk of developing clinical manifestations of ASD increases with a decrease in the age of onset of epileptic seizures, decrease in the frequency and amplitude of the alpha rhythm according to electroencephalography (EEG) data, increase of the index of spike-wave activity, frequency and amplitude of epileptiform activity, increase in the index of fractional anisotropy in the anterior part of the arcuate tract, decrease in the average diffusion coefficient in the area of the Broca’s center, fractional anisotropy in the Wernicke center and the knee of the corpus callosum according to magnetic resonance tractography. Conclusions. A regression model for predicting the risk of clinical manifestations of ASD in children with EE was developed with an average error of approximation of 13.2% and a coefficient of determination of 0.74, which is recommended for use in clinical practice in order to form groups of children at high risk of ASD. Such children require further dynamic monitoring and early intervention by specialists of a multidisciplinary team for timely identification and correction of ASD symptoms The research was carried out in accordance with the principles of the Helsinki Declaration. The study protocol was approved by the Local Ethics Committee of the participating institution. The informed consent of the patient was obtained for conducting the studies. No conflict of interests was declared by the author.

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“…Previous research has shown that the brain's ability to change and adapt (neural plasticity) declines with age. Early intervention for children with autism can help to improve their language and cognitive abilities before the onset of behavioural problems [5]. Hence, the timely identification of ASD holds substantial importance.…”

Section: Introductionmentioning

confidence: 99%

Novel Framework for Autism Spectrum Disorder Identification and Tailored Education With Effective Data Mining and Ensemble Learning Techniques

Hajjej,

Ayouni,

Alohali

et al. 2024

IEEE Access

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Autism Spectrum Disorder (ASD) is a neurological condition that significantly affects cognitive abilities, language comprehension, object recognition, interpersonal skills, and communication capabilities. Its primary origin is genetic, and early detection and intervention can mitigate the need for costly medical procedures and lengthy examinations for individuals affected by ASD. An effective teaching approach may be challenging to determine for a child with autism. Autism Spectrum Disorder is highly diverse, with each affected child being unique. It is often stated that no two autistic children are alike, meaning that what benefits one child may not be suitable for another. Two ASD screening datasets of toddlers are merged in this study. The SMOTE method to balance the dataset, followed by feature selection methods. The research introduces a two-phase system: the first phase employs various machine learning models, including an ensemble of random forest and XGBoost classifiers, for accurate ASD identification. In the second phase, the study focuses on identifying appropriate teaching methods for children with ASD by evaluating their physical, verbal, and behavioural performance. This research aims to provide personalized educational approaches for individuals with ASD, harnessing machine learning to enhance precision in addressing their unique needs.

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Early Prediction and Evaluation of Risk of Autism Spectrum Disorders

Cited by 10 publications

References 47 publications

Screening for Developmental Behavioural Disorders

Screening for Developmental Behavioural Disorders

Prediction of the risk of development of autistic spectrum disorders in children with epileptic encephalopathies

Novel Framework for Autism Spectrum Disorder Identification and Tailored Education With Effective Data Mining and Ensemble Learning Techniques

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